In this study, we propose a supervised learning approach to automatically learn a variable ordering heuristic for solving job shop scheduling (JSS) problems [1]}. JSS is a class of NP-hard combinatorial optimization problems that often arise in manufacturing [1]}. In a JSS problem, there are a number of jobs, each consisting of some operations to be processed on a number of machines. The aim of JSS is to schedule operations on machines such that constraints are satisfied and an objective such as makespan or total weighted tardiness is optimised (see Section  for a formal definition). JSS has attracted much attention in the research community [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}.
